CN105336998A - Intelligent charging method for electric vehicle of considering user fees and life lost of transformer - Google Patents

Abstract

The invention relates to an intelligent charging method for an electric vehicle when the electric vehicle is accessed into a power distribution network. On the basis of a charger used every day, a charging control management system is added; the influences of user fees and life lost of a transformer are considered; difference between peak and valley prices is considered from two aspects of safety and economy; and the minimal electric charge of a user and the minimal life lost of a distribution transformer are simultaneously taken as target functions for optimization. The method is a dynamic cycle optimization method. The randomness of charge starting time of the electric vehicle is considered; and the optimal charging rate (charging current) of the electric vehicle which is accessed into the power distribution network is automatically designed through an optimization program, so that the target of reducing the life lost of the distribution transformer and the user fees is reached; and the operating efficiency of the distribution transformer is improved.

Description

Consider the Intelligent charging method for electromobile of customer charge and transformer life loss

Technical field:

The invention belongs to the correlation technique of charging system for electric automobile design, particularly a kind of intelligent charging method of automatic adjustment charging electric vehicle electric current, this method is a kind of dynamic circulation optimization method, consider the randomness of charging electric vehicle initial time, the optimum rate of charge (charging current) of the electric automobile of electrical network is gone out to access by optimizer Automated Design, reduce the life loss of distribution transformer and the object of customer charge to reach, thus improve the operational efficiency of distribution transformer.

Background technology:

The access of a large amount of electric automobile can run power distribution network and produce a very large impact, and can increase the load of power distribution network, affect to distribution transformer thermal capacity and useful life thereof.For electric automobile, the aspects such as charging device, distribution network electric energy quality and charging station planning are mainly concentrated on to electric network influencing research at present, and for less to the influence research of power distribution network main equipment, and the charging method of electric automobile considered greatly by existing document mainly with single goal, and attend to one thing and lose sight of another unavoidably based on the control method of single goal; Such as, minimum for the life loss control method as target will be caused the increase of user cost; New peak value may be produced with the minimum control method for target of user cost; And only control the randomness that the method for charging electric vehicle time fails to consider the charging electric vehicle time, therefore this intelligent charging method considers the regularity of charging electric vehicle time, the charging current of electric automobile is controlled in advance, as optimization object while that minimum for demand charge and distribution transformer life loss is minimum, set up a multiobject majorized function.

Summary of the invention:

The present invention relates to the intelligent charging method that a kind of a large amount of electric automobile accesses power distribution network simultaneously, on daily charger used basis, a kind of Intelligent charging method for electromobile considering customer charge and transformer life impact, the optimum charging method of multiple stage electric automobile in a day is gone out by the optimizer Automated Design of Intelligent charging management system, the charging current of automatic adjustment electric automobile, to reduce life loss and the customer charge of distribution transformer.

Concrete technical scheme is as follows:

Consider an Intelligent charging method for electromobile for customer charge and transformer life impact, comprise the following steps:

(1) i-th electric automobile access socket, prepares charging (i=1,2...N);

(2) by electrical network Real-time Load monitoring system, the load variations curve in the past of electrical network is obtained; According to relevant informations such as the load variations in the past of electrical network, Intelligent charging management system is predicted and is simulated certain the distribution transformer power load curve from current by following 24 hours;

(3) the variation of ambient temperature curve from current by following 24 hours is obtained;

(4) Intelligent charging management system is according to related datas such as the load in 24 hours futures of prediction, ambient temperatures residing for transformer, is automatically calculated the optimum charging current of i-th electric automobile by optimizer;

(5) Intelligent charging management system controls closed charging main relay, according to the output control charging electric vehicle electric current of optimizer;

(6) actual charging electric vehicle load is added in Real-time Load by intelligent management system, calculates from current to the life loss of following 24 hours, the electricity charge;

(7) i-th electric automobile optimizations terminate, and are optimized the i-th+1 charging electric vehicle electric current, forward step (1) to;

After (8) i-th charging electric vehicles reach requirement, charger charging main relay disconnects, complete charge.

Intelligent charging management system is a kind of dynamic circulation optimization method, considers the randomness of charging electric vehicle initial time, by optimizer progressively Automated Design go out to access the optimum rate of charge (charging current) of many electric automobiles of electrical network.

Preferably, in step (2), according to the relevant information such as load variations, environmental factor, social factor in the past of electrical network, Intelligent charging management system adopts method prediction certain distribution transformer power load curve of following 24 hours of SVMs.

Preferably, in step (3), obtain the method from current to the ambient temperature of following 24 hours: the ambient temperature rule of Intelligent charging management system according to 2 years in the past and the ambient temperature prediction of recent meteorological department, carry out the ambient temperature of the following 24 hours distribution transformers of comprehensive analyses and prediction.

Preferably, in step (4), the type of the electric automobile that Intelligent charging management system controls is single type electric motor car, has the parameters such as identical battery types, capacity, charge volume.

Preferably, in step (4), the optimizer of Intelligent charging management system utilizes adaptivity population, controls the charging current of electric automobile, and final purpose makes distribution transformer while meeting consumers' demand, make life loss minimum.

Particle cluster algorithm is initialized as a group random particles (RANDOM SOLUTION), then finds optimal solution by iteration.In each iteration, particle upgrades oneself by tracking two " extreme values ".First optimal solution being exactly particle itself and finding, this solution is called individual extreme value pBest, i.e. P _{i, j}.Another extreme value is the optimal solution that whole population is found at present, and this extreme value is global extremum gBest, i.e. P _{g, j}.After finding two extreme points, particle comes renewal speed and position according to formula (1) (2):

v _i，j(t+1)＝wv _i，j(t)+c ₁r ₁[p _i，j-x _i，j(t)]+c ₂r ₂[p _g，j-x _i，j(t)](1)

x _i，j(t+1)＝x _i，j(t)+v _i，j(t+1)(2)

Wherein w is Inertia weight factor, c ₁, c ₂for positive Studying factors, r ₁, r ₂it is equally distributed random number between 0 to 1.

Preferably, in step (4), in order to ability of searching optimum and the local improved abilities of equilibrium particle group algorithm, what used is modified particle swarm optiziation---APSO algorithm, nonlinear dynamic inertia weight coefficient formula is adopted to ask for the adaptive weighting automatically changed along with the target function value of particulate, wherein w according to formula (3) _min, w _maxrepresent minimum value and the maximum of inertia weight respectively, f is the current target function value of particle, f _avg, f _minfor average target value and the minimum target value of current all particles.

$w=\left\{\begin{array}{c}{w}_{\max },f>f_{\mathrm{avg}}\\ w_{\min }-\frac{(w_{\max }-w_{\min })*(f-f_{\min }),}{(f_{\mathrm{avg}}-f_{\min })}f\≤f_{\mathrm{avg}}\end{array}\right.---\left(3\right)$

Preferably, in step (4), APSO algorithm is the particle cluster algorithm of integer, the charge power control method of batteries of electric automobile is Current Control, the suitable charging current scope of batteries of electric automobile is 0.2C ~ 0.3C (C represents the capacity of battery), in this intelligent method, desirable charging current is 0.2C, 0.3C, 0.4C, 0.5C, 0.6C, 0.7C, 0.8C, 0.9C, 1.0C, according to charging batteries of electric automobile rule, approximate charge power is reduced to constant, the rated voltage of the desirable batteries of electric automobile group of voltage, and charging batteries of electric automobile amount is from 0 to 100%, one day 24 hours every 3 minutes are got a spaced points to continue to optimize and calculate.

Preferably, in step (4), in order to meet the demand of user's charging electric vehicle, the multiple-objection optimization intelligent charging method proposed is optimized from fail safe and economy two aspect simultaneously, economy is from user perspective, and meeting, the electricity charge under the prerequisite needed for user are minimum; Fail safe, from the safety and stability of power distribution network, makes distribution transformer life loss minimum, constructs the electric automobile multiple target charging Optimized model of the impact simultaneously considering time-of-use tariffs and distribution transformer life loss.

(1) target function 1: life loss least model

Life loss optimal control intelligent charging method, final purpose makes distribution transformer while meeting consumers' demand, make life loss minimum, this intelligent charging method makes formula (4) set up, and the life loss of transformer calculates according to IEC standard;

$\min L=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}V(\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{I}_j,p_{i,j},t_j,{\mathrm{\θ}}_a,\mathrm{\Δ}{t}_i.L_i)---\left(4\right)$

Wherein, I _jbeing the charging current of a jth electric automobile, is also the amount that this patent will be optimized; t _jit is the initiation of charge time of a jth electric automobile; Charging interval 6pm to 7am is divided into the N section evenly equal time, is divided into the time that N section is evenly equal, and i is i-th period of time interval, and j is the electric automobile of a jth access, p _{i, j}the charge power of a jth electric automobile i-th time interval, the life loss of electric automobile in i-th time interval of all accesses;

(2) target function 2: demand charge least model

Suppose that the pilot work of resident living power utility Peak-valley TOU power price is carried out in residential quarter, paddy peak electricity price will calculate the electricity charge according to the principle of " first timesharing, rear ladder ", but only study the residential block power load of a day herein, so temporarily do not consider step price, wherein, peak of power consumption section is that every day 8 is up to 22 time; Low power consumption section is 22 up to next day 8 time;

Demand charge least model, final purpose makes distribution transformer while meeting consumers' demand, make the electricity charge of user minimum, even if (5) set up;

$\min F=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}Y(\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{I}_j,p_{i,j},\mathrm{\Δ}{t}_i,t,d)---\left(5\right)$

$d=\left\{\begin{array}{c}\mathrm{0.55,8}:00\≤t\≤22:00\\ \mathrm{0.35,0}\≤t\≤8:\mathrm{00,22}:00\≤t\≤24:00\end{array}\right.---\left(6\right)$

D is electricity price, and during peak of power consumption, electricity price is every kilowatt hour 0.55 yuan, and during low power consumption, electricity price is every kilowatt hour 0.35 yuan; the electricity charge of electric automobile in i-th time interval of all accesses;

(3) constraints

Above-mentioned two target functions need meet constraints (7) (8) (9) (10) simultaneously:

$s.t.\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{p}_{i,j}\mathrm{\Δ}{t}_i=C---\left(7\right)$

I _min≤I _j≤I _max(8)

t _min≤t _j≤t _max(9)

0≤t≤24：00(10)

C is each charging electric vehicle capacity, I _minthe minimum current of charging electric vehicle, I _maxthe maximum current of charging electric vehicle, t is the time;

(4) based on the electric automobile multiple-objection optimization intelligent charge model of time-of-use tariffs and life loss

minFF＝(1-p)*L+p*F(11)

Formula (11) is consider the electric automobile multiple-objection optimization intelligent charge model of time-of-use tariffs and life loss simultaneously, the minimum disaggregation of this formula is made to be the optimal solution of Optimized model, wherein p, 1-p are the weight of demand charge and life loss, 0 < p < 1, desirable p=0.4.

Preferably, in step (4), the life loss of transformer calculates according to IEC newest standards.

Accompanying drawing illustrates:

Fig. 1 is a kind of system construction drawing simultaneously considering the Intelligent charging method for electromobile of customer charge and transformer life impact provided by the invention;

Fig. 2 is a kind of Optimizing Flow figure simultaneously considering the Intelligent charging method for electromobile of customer charge and transformer life impact provided by the invention.

Embodiment:

1,2 couples of the present invention are described in detail with reference to the accompanying drawings below, and it is a kind of preferred embodiment in numerous embodiments of the present invention.

A kind of Intelligent charging method for electromobile considering customer charge and transformer life impact provided by the invention, system configuration is as Fig. 1, and onboard charger of electric car is connected with electrical network by Intelligent charging management system;

When electric car charger is connected with electrical network by charging wire and charging socket, this method is a kind of dynamic circulation optimization method, consider the randomness of charging electric vehicle initial time, the optimum rate of charge (charging current) of the electric automobile of electrical network is gone out to access by optimizer Automated Design, reduce the life loss of distribution transformer and the object of customer charge to reach, thus improve the operational efficiency of distribution transformer;

After arriving when charging, Intelligent charging management system closed charging main relay, starts charging according to optimizer result; Intelligent charging system detects batteries of electric automobile electricity, after charging reaches requirement, and automatic complete charge.

Concrete Optimizing Flow is as Fig. 2:

(1) i-th electric automobile access socket, prepares charging (i=1,2...N);

(2) Intelligent charging management system is connected with electrical network Real-time Load monitoring system, obtains electrical network Real-time Load change curve; According to the relevant information such as load variations, environmental factor, social factor in the past of electrical network, Intelligent charging management system adopts the method prediction distribution transformer power load situation of following 24 hours of SVMs;

(3) Intelligent charging management system obtains the method from current to the ambient temperature of following 24 hours: the ambient temperature according to the ambient temperature rules of 2 years in the past and recent meteorological department is predicted, carries out the ambient temperature of the following 24 hours distribution transformers of comprehensive analyses and prediction;

(4) Intelligent charging management system calculates the optimum charging current of the electric automobile being about to access automatically by optimizer; In order to meet the demand of user's charging electric vehicle, the multiple-objection optimization intelligent charging method proposed is optimized from fail safe and economy two aspect simultaneously, and economy is from user perspective, and meeting, the electricity charge under the prerequisite needed for user are minimum; Fail safe, from the safety and stability of power distribution network, makes distribution transformer life loss minimum, constructs the electric automobile multiple target charging Optimized model of the impact simultaneously considering time-of-use tariffs and distribution transformer life loss;

Utilize adaptivity population, control be optimized to the charging current of electric automobile, final purpose be make distribution transformer make while meeting consumers' demand customer charge and distribution transformer life loss minimum;

The type of the electric automobile that Intelligent charging management system controls is single type electric motor car, has identical battery types, capacity, charge volume etc.;

APSO algorithm is the particle cluster algorithm of integer, the charge power control method of batteries of electric automobile is Current Control, the suitable charging current scope of batteries of electric automobile is 0.2C ~ 0.3C (C represents the capacity of battery), and in this intelligent method, desirable charging current is 0.2C, 0.3C, 0.4C, 0.5C, 0.6C, 0.7C, 0.8C, 0.9C, 1.0C; According to charging batteries of electric automobile rule, approximate charge power is reduced to constant, the rated voltage of the desirable batteries of electric automobile group of voltage, and charging batteries of electric automobile amount is from 0 to 100%;

One day 24 hours every 3 minutes are got a spaced points to continue to optimize and calculate;

(5) Intelligent charging management system is according to optimum results scheduling charger charging main relay, starts charging;

(6) actual charging electric vehicle load is added in Real-time Load by Intelligent charging management system, calculates life loss and the electricity charge of one day 24 hours;

(7) i-th electric automobile optimizations terminate, and are optimized the i-th+1 charging electric vehicle electric current, forward step (1) to;

After (8) i-th charging electric vehicles reach requirement, charger charging main relay disconnects, complete charge.

The above is only the preferred embodiment of the present invention; it should be pointed out that for those skilled in the art, under the prerequisite not departing from the inventive method; can also make some improvement and supplement, these improve and supplement and also should be considered as protection scope of the present invention.

Claims (10)

1. consider an Intelligent charging method for electromobile for customer charge and transformer life impact, it is characterized in that, comprise the following steps:

(1) i-th electric automobile access socket, prepares charging (i=1,2...N, i are electric automobile quantity);

(2) by electrical network Real-time Load monitoring system, the load variations curve in the past of electrical network is obtained; According to relevant informations such as the load variations in the past of electrical network, Intelligent charging management system is predicted and is simulated certain the distribution transformer power load curve from current by following 24 hours;

(3) the variation of ambient temperature curve from current by following 24 hours is obtained;

(4) Intelligent charging management system is according to related datas such as the load in 24 hours futures of prediction, ambient temperatures residing for transformer, is automatically calculated the optimum charging current of i-th electric automobile by optimizer;

(5) Intelligent charging management system controls closed charging main relay, according to the output control charging electric vehicle electric current of optimizer;

(6) actual charging electric vehicle load is added in Real-time Load by intelligent management system, calculates from current to the life loss of following 24 hours, the electricity charge;

(7) i-th electric automobile optimizations terminate, and are optimized the i-th+1 charging electric vehicle electric current, forward step (1) to;

After (8) i-th charging electric vehicles reach requirement, charger charging main relay disconnects, complete charge.

2. the Intelligent charging method for electromobile considering customer charge and transformer life impact as claimed in claim 1, it is characterized in that, Intelligent charging management system is a kind of dynamic circulation optimization method, consider the randomness of charging electric vehicle initial time, by optimizer progressively Automated Design go out to access the optimum rate of charge (charging current) of many electric automobiles of electrical network.

3. the Intelligent charging method for electromobile considering customer charge and transformer life impact as claimed in claim 1, it is characterized in that, in step (2), according to the relevant information such as load variations, environmental factor, social factor in the past of electrical network, Intelligent charging management system adopts the correlation technique prediction distribution transformer power load situation of following 24 hours of load prediction.

4. the Intelligent charging method for electromobile considering customer charge and transformer life impact as claimed in claim 1, it is characterized in that, in step (3), obtain the method from current to the ambient temperature of following 24 hours: the ambient temperature rule of Intelligent charging management system according to 2 years in the past and the ambient temperature prediction of recent meteorological department, carry out the ambient temperature of the following 24 hours distribution transformers of comprehensive analyses and prediction.

5. the Intelligent charging method for electromobile considering customer charge and transformer life impact as claimed in claim 1, it is characterized in that, in step (4), the type of the electric automobile that Intelligent charging management system controls is single type electric motor car, has the parameters such as identical battery types, capacity, charge volume.

6. the Intelligent charging method for electromobile considering customer charge and transformer life impact as claimed in claim 1, it is characterized in that, in step (4), the optimizer of Intelligent charging management system, utilize adaptivity population, control is optimized to the charging current of electric automobile, final purpose be make distribution transformer make while meeting consumers' demand customer charge and distribution transformer life loss minimum;

Particle cluster algorithm is initialized as a group random particles (RANDOM SOLUTION), then finds optimal solution by iteration, and in each iteration, particle upgrades oneself by tracking two " extreme values "; First optimal solution being exactly particle itself and finding, this solution is called individual extreme value pBest, i.e. P _{i, j}; Another extreme value is the optimal solution that whole population is found at present, and this extreme value is global extremum gBest, i.e. P _{g, j}; After finding two extreme points, particle comes renewal speed and position according to formula (1) (2):

v _i，j(t+1)＝wv _i，j(t)+c ₁r ₁[p _i，j-x _i，j(t)]+c ₂r ₂[p _g，j-x _i，j(t)](1)

x _i，j(t+1)＝x _i，j(t)+v _i，j(t+1)(2)

Wherein w is Inertia weight factor, c ₁, c ₂for positive Studying factors, r ₁, r ₂it is equally distributed random number between 0 to 1.

7. the Intelligent charging method for electromobile considering customer charge and transformer life impact as claimed in claim 1, it is characterized in that, in step (4), in order to ability of searching optimum and the local improved abilities of equilibrium particle group algorithm, what used is modified particle swarm optiziation---APSO algorithm, nonlinear dynamic inertia weight coefficient formula is adopted to ask for the adaptive weighting automatically changed along with the target function value of particulate, wherein w according to formula (3) _min, w _maxrepresent minimum value and the maximum of inertia weight respectively, f is the current target function value of particle, f _avg, f _minfor average target value and the minimum target value of current all particles.

$w=\left\{\begin{array}{c}{w}_{\max },f>f_{\mathrm{avg}}\\ w_{\min }-\frac{(w_{\max }-w_{\min })*(f-f_{\min }),}{(f_{\mathrm{avg}}-f_{\min })}f\&le;f_{\mathrm{avg}}\end{array}\right.---\left(3\right)$

8. the Intelligent charging method for electromobile considering customer charge and transformer life impact as claimed in claim 1, it is characterized in that, in step (4), APSO algorithm is the particle cluster algorithm of integer, the charge power control method of batteries of electric automobile is Current Control, the suitable charging current scope of batteries of electric automobile is 0.2C ~ 0.3C (C represents the capacity of battery), and in this intelligent method, desirable charging current is 0.2C, 0.3C, 0.4C, 0.5C, 0.6C, 0.7C, 0.8C, 0.9C, 1.0C;

According to charging batteries of electric automobile rule, approximate charge power is reduced to constant, the rated voltage of the desirable batteries of electric automobile group of voltage, and charging batteries of electric automobile amount is from 0 to 100%; One day 24 hours every 3 minutes are got a spaced points to continue to optimize and calculate.

9. the Intelligent charging method for electromobile considering customer charge and transformer life impact as claimed in claim 1, it is characterized in that, in step (4), in order to meet the demand of user's charging electric vehicle, the multiple-objection optimization intelligent charging method proposed is optimized from fail safe and economy two aspect simultaneously, economy is from user perspective, and meeting, the electricity charge under the prerequisite needed for user are minimum; Fail safe, from the safety and stability of power distribution network, makes distribution transformer life loss minimum, constructs the electric automobile multiple target charging Optimized model of the impact simultaneously considering time-of-use tariffs and distribution transformer life loss;

(1) target function 1: life loss least model

Life loss optimal control intelligent charging method, final purpose makes distribution transformer while meeting consumers' demand, make life loss minimum, this intelligent charging method makes formula (4) set up, and the life loss of transformer calculates according to IEC standard;

$\min L=\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}V(\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{I}_j,p_{i,j},t_j,{\mathrm{\&theta;}}_a,\mathrm{\&Delta;}{t}_i.L_i)---\left(4\right)$

Wherein, I _jbeing the charging current of a jth electric automobile, is also the amount that this patent will be optimized; t _jit is the initiation of charge time of a jth electric automobile; Charging interval 6pm to 7am is divided into the N section evenly equal time, is divided into the time that N section is evenly equal, and i is i-th period of time interval, and j is the electric automobile of a jth access, p _{i, j}the charge power of a jth electric automobile i-th time interval, the life loss of electric automobile in i-th time interval of all accesses;

(2) target function 2: demand charge least model

Suppose that the pilot work of resident living power utility Peak-valley TOU power price is carried out in residential quarter, paddy peak electricity price will calculate the electricity charge according to the principle of " first timesharing, rear ladder ", but only study the residential block power load of a day herein, so temporarily do not consider step price, wherein, peak of power consumption section is that every day 8 is up to 22 time; Low power consumption section is 22 up to next day 8 time;

Demand charge least model, final purpose makes distribution transformer while meeting consumers' demand, make the electricity charge of user minimum, even if (5) set up;

$\min F=\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}Y(\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{I}_j,p_{i,j},\mathrm{\&Delta;}{t}_i,t,d)---\left(5\right)$

$d=\left\{\begin{array}{c}\mathrm{0.55,8}:00\&le;t\&le;22:00\\ \mathrm{0.35,0}\&le;t\&le;8:\mathrm{00,22}:00\&le;t\&le;24:00\end{array}\right.---\left(6\right)$

D is electricity price, and during peak of power consumption, electricity price is every kilowatt hour 0.55 yuan, and during low power consumption, electricity price is every kilowatt hour 0.35 yuan; the electricity charge of electric automobile in i-th time interval of all accesses;

(3) constraints

Above-mentioned two target functions need meet constraints (7) (8) (9) (10) simultaneously:

$s.t.\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{p}_{i,j}\mathrm{\&Delta;}{t}_i=C---\left(7\right)$

I _min≤I _j≤I _max(8)

t _min≤t _j≤t _max(9)

0≤t≤24：00(10)

C is each charging electric vehicle capacity, I _minthe minimum current of charging electric vehicle, I _maxthe maximum current of charging electric vehicle, t is the time;

(4) based on the electric automobile multiple-objection optimization intelligent charge model of time-of-use tariffs and life loss

minFF＝(1-p)*L+p*F(11)

Formula (11) is consider the electric automobile multiple-objection optimization intelligent charge model of time-of-use tariffs and life loss simultaneously, the minimum disaggregation of this formula is made to be the optimal solution of Optimized model, wherein p, 1-p are the weight of demand charge and life loss, 0 < p < 1, desirable p=0.4.

10. the Intelligent charging method for electromobile considering customer charge and transformer life impact as claimed in claim 1, it is characterized in that, in step (8), Intelligent charging management system Real-Time Monitoring charging electric vehicle state, after charging electric vehicle reaches requirement, charger charging main relay disconnects, complete charge.